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SIMD lets one operation work on several data values at once. In Mojo, the SIMD[dtype, width] type makes that vector’s element type and number of lanes explicit, and supported operations apply lane by lane. Writing a SIMD expression enables this programming model; it does not guarantee a speedup, which depends on the hardware, workload, compiler, and measurement.
What SIMD means
SIMD stands for “single instruction, multiple data.” Instead of expressing an operation for one value at a time, SIMD expresses the same operation across several values. Processors can execute vector instructions over multiple values held in vector registers. The number of values useful in parallel depends on the target hardware and the operation.
How Mojo represents a SIMD vector
Mojo’s standard-library type is written SIMD[dtype, width]. The dtype specifies the kind of each element; the width specifies how many elements, or lanes, the vector contains. Both are part of the type, rather than metadata supplied at runtime, and the width must be a power of two.
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Width is a compile-time limit, not a hardware recommendation
The numeric types reference documents a hard compile-time SIMD-width limit of 2^15 (32,768) elements. That limit is not a practical vector-width recommendation. Useful widths are constrained by the target hardware, and a vector wider than its capabilities may not perform as expected. The documentation also gives four, eight, and 16 values as examples of counts modern CPUs may process in parallel; these are examples, not a universal guarantee for every CPU, dtype, or workload.
What happens when you operate on SIMD values
When an operation supports SIMD values, Mojo applies it to corresponding lanes. For example, multiplying two four-element integer vectors produces four products: lane zero is multiplied by lane zero, lane one by lane one, and so on. The operation does not combine the vectors into a matrix multiplication.
Rank #2
For the documented arithmetic operators, operands need the same dtype and vector size. Mojo does not automatically widen a lower-precision operand to match a higher-precision one; explicitly cast a value when you need to change its type. Operator availability also depends on dtype: the documentation describes arithmetic for numeric SIMD values, while bitwise operators apply to integral or boolean vectors. Check the supported operation for the type you are using rather than assuming every operator works on every SIMD type.
How SIMD relates to Mojo scalar types
A one-lane SIMD value is a Scalar. Fixed-width scalar names such as Float32 are aliases for one-lane SIMD types. This gives scalar and vector values a shared numeric foundation, even though a scalar expresses one lane and a wider SIMD value expresses several.
Rank #3
When to use SIMD directly or a higher-level algorithm
For a small elementwise operation, direct SIMD values make the lanes and operation explicit. For larger data-parallel kernels, Mojo’s algorithm package provides primitives for vectorization, parallelization, and reduction. Its documentation positions these tools for large datasets or compute-intensive work; for small elementwise work, an ordinary loop may be simpler.
- Use direct SIMD expressions when you want to work with a fixed-size vector and the operation is supported for its dtype.
- Consider the algorithm primitives when the task involves larger-scale data parallelism, parallel execution, or reductions.
- Keep a simple loop when its clarity suits the workload; SIMD syntax is not an end in itself.
How to judge whether a SIMD width helps
Do not treat a wider vector as automatically faster. The useful width and performance depend on the hardware, workload, and compiler lowering. Modular’s Mojo numeric types reference advises: “Always benchmark to find the optimal width for your workload and target hardware.”
Compare the same useful work on the intended target, and assess the results for the workload you actually need to run. A SIMD type tells you how many lanes your code expresses; benchmarking tells you whether that choice helps in practice. The reviewed documentation does not establish a universal best width or a performance figure for a particular machine or program.
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Documentation
- Mojo SIMD documentation explains the fixed-size vector type and supported operations.
- Mojo numeric types reference covers SIMD widths, scalar aliases, and hardware-dependent performance guidance.
- Mojo operators documentation describes operator behavior and operand requirements.
- Mojo algorithm package documentation describes higher-level vectorization, parallelization, and reduction primitives.
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